class_docstr: Apply gaussian blur given tensor image or a batch of tensor images randomly. The standard deviation is
  sampled for each instance.
  
  .. image:: _static/img/RandomGaussianBlur.png
  
  Args:
      kernel_size: the size of the kernel.
      sigma: the range for the standard deviation of the kernel.
      border_type: the padding mode to be applied before convolving.
        The expected modes are: ``constant``, ``reflect``, ``replicate`` or ``circular``.
      separable: run as composition of two 1d-convolutions.
      same_on_batch: apply the same transformation across the batch.
      p: probability of applying the transformation.
      keepdim: whether to keep the output shape the same as input (True) or broadcast it
               to the batch form (False).
      silence_instantiation_warning: if True, silence the warning at instantiation.
  
  Shape:
      - Input: :math:`(C, H, W)` or :math:`(B, C, H, W)`, Optional: :math:`(B, 3, 3)`
      - Output: :math:`(B, C, H, W)`
  
  .. note::
      This function internally uses :func:`kornia.filters.gaussian_blur2d`.
  
  Examples:
      >>> rng = torch.manual_seed(0)
      >>> input = torch.rand(1, 1, 5, 5)
      >>> blur = RandomGaussianBlur((3, 3), (0.1, 2.0), p=1.)
      >>> blur(input)
      tensor([[[[0.5941, 0.5833, 0.5022, 0.4384, 0.3934],
                [0.5310, 0.4964, 0.4113, 0.3637, 0.3472],
                [0.4991, 0.4997, 0.4312, 0.3620, 0.3081],
                [0.6082, 0.5667, 0.4954, 0.3825, 0.3508],
                [0.7042, 0.6849, 0.6275, 0.4753, 0.4105]]]])
  
  To apply the exact augmenation again, you may take the advantage of the previous parameter state:
      >>> input = torch.randn(1, 3, 32, 32)
      >>> aug = RandomGaussianBlur((3, 3), (0.1, 2.0), p=1.)
      >>> (aug(input) == aug(input, params=aug._params)).all()
      tensor(True)
code: def apply_transform(
      self,
      input: Tensor,
      params: Dict[str, Tensor],
      flags: Dict[str, Any],
      transform: Optional[Tensor] = None,
  ) -> Tensor:
      sigma = (
          params["sigma"]
          .to(device=input.device, dtype=input.dtype)
          .unsqueeze(-1)
          .expand(-1, 2)
      )
      return gaussian_blur2d(
          input,
          self.flags["kernel_size"],
          sigma,
          self.flags["border_type"].name.lower(),
          separable=self.flags["separable"],
      )
  
file_path: kornia/kornia/augmentation/_2d/intensity/gaussian_blur.py
imports: import warnings
  from typing import Any, Dict, Optional, Tuple, Union
  from torch import Tensor
  from kornia.augmentation import random_generator
  from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
  from kornia.constants import BorderType
  from kornia.filters import gaussian_blur2d
label_desc: Use image node_id 2 for calling the RandomGaussianBlur obj's underlying member method code with example usage: obj.apply_transform(input, params, flags, transform) and returns: gaussian_blur2d